Procurement organizations often conduct ecommerce auctions to identify the most preferred suppliers for goods and services that the procurement organization is looking to procure. These ecommerce auctions often involve multiple goods and/or service items, and often many suppliers are invited to participate in the auction. In an ecommerce auction, the frequency of bid updates from suppliers tends to increase as the auction nears completion and the bidding gets more and more competitive. Even as the auction nears completion and activity increases, the buyers within the procurement organizations have to keep track of incoming bids from suppliers, determine the impact of new bids, calculate or estimate the effect on the relative rankings between bidding suppliers, calculate or estimate projected savings, and so on.
Based on such fast-changing information, the buyer may be compelled to take necessary actions such as pause the auction, extend the remaining time duration, etc. In fast paced auctions, it becomes very difficult for the buyer to absorb the fast-changing information (e.g., the changes to incoming bids) and assess their impact to the organization and to the supplier selection process. Legacy systems have rudimentary bid monitoring capabilities intended to show data regarding incoming bids. However, legacy systems fall short of aiding the buyer in order to show (e.g., via a highlight or emphasis) the changed bid, and to show (e.g., via a highlight emphasis) the impact of the changed bids. Moreover, towards the close of the auction this information is ever more fast-changing, making it nearly impossible for the user to manually figure out what information had changed and interpret the impact or impacts resulting from the changed information.
Therefore, there is a need for an improved approach involving fast-paced dynamic monitoring of changing data in a real-time online auction using interpretive data change indicators.